Postdoc: Terrestrial Carbon Cycle Data Assimilation

A post-doctoral position is available in the Ecological Forecasting lab at Boston University.

Date posted

Sep. 22, 2017 12:00 am

Application deadline

Nov. 22, 2017 12:00 am

Organization

Boston University

Location

United States

Job description

A post-doctoral position is available in the Ecological Forecasting lab at Boston University as part of a larger project to develop a terrestrial carbon cycle data assimilation system, focused initially on North America, using the PEcAn model informatics system (pecanproject.org). This system will employ a formal Bayesian model-data fusion between bottom-up process-based ecosystem models and multiple data sources to estimate key C pools and fluxes.

Duties:Candidate will work with collaborators at Brookhaven to iteratively extend the PEcAn data assimilation system to ingest a wide range of remotely-sensed and ground data with the goal of fusing and reconciling multiple data streams into a continental-extent carbon cycle (pools and fluxes) data product. They will be responsible for incorporate new scaling approaches into the data assimilation system itself and extending the assimilation to work with multiple land surface models. They will contribute to analyses assessing the impacts of different data sources and models on carbon pool and flux estimates and uncertainties, with the aim of improving carbon monitoring, reporting, and verification. Finally, the candidate will also assist collaborators at NOAA who will be incorporating the bottom-up assimilation product into the CarbonTracker-Lagrange (CT-L) inverse modeling framework to help reconcile top-down and bottom-up flux estimates.

Qualifications:Minimum qualifications are a doctoral degree in a related environmental science (ecology, geography, atmospheric science, earth science, etc.). Experience with R and at least one of the following topics is required (along with interest in learning the others): Bayesian statistics, ensemble filtering approaches (e.g. EnKF), ecosystem or land surface modeling, remote sensing, and ecoinformatics. Salary is commensurate with experience and qualifications. Three years of funding available.